Degrees and courses in Data Science
A year ago, I saw this announcement inside Harvard Yard:
Right now (May 2019) undergraduates at Harvard still can't major in Data Science, but they can take related courses in departments including Statistics, Computer Science and - as the photo proves - Government.
Interestingly, if you are high school student you could think about pursuing one of the following:
And there are dozens of similar options out there for undergraduates. I was thrilled to see that NYU will not offer a Data Science major too:
NIST (2015, p. 7) describes data science as “the extraction of actionable knowledge directly from data through a process of discovery, or hypothesis formulation and hypothesis testing.”
Prospective graduate students have had a rich set of choices for a while at NYU, specifically MS in Data Science, and an exciting option: PhD in Data Science at the NYU Center for Data Science. And advanced students do have options at Harvard, including
In a viral thread Rochelle Terman asked a few days "When teaching R, how far to you go? Should you cover object-oriented programming, compiling in C, assembly, electric circuits and motherboards?"
2) What is the end goal of methods training? Which people deserve to be taught? What is the intention behind teaching "technical details", and how does it actually function with our students? 17/n— Rochelle Terman (@RochelleTerman) May 10, 2019
In my opinion, the beauty of many data science courses is precisely that some technical aspects are skipped when there isn't a clear a pedagogical justification for them.
What Terman describes as computational social science (CSS) encapsulates proficiency in R, python, git, webscraping, web development, machine learning, text-as-data. Add to that visualization and story-telling, and CSS starts to look a lot like data science. A very valuable set of skills.
Here is Henry E. Brady's recommendation:
Political science professors must develop new courses and become conversant with the new technologies developed by data scientists. New courses should go in two directions. One course should deal with the societal challenges of big data and what they mean for politics. Mergel (2016) has
developed a curriculum for schools of public affairs which contains some pertinent elements, including sections on big data in politics, government, public health, and smart cities, but it does not have a section on the media, and it does not directly focus on the political issues such as data ownership and use, privacy, and loss of jobs that stem from big data.
A second course must teach students data science methods.